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Vehicle Trajectory Prediction Method Based on Graph Convolutional Interaction Network
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Mengxi Wang1, Yingfeng Cai1, Hai Wang2, Zhongyu Rao1, Long Chen1, Yicheng Li1
Automotive Engineering | 2024, 46(10) : 1863 - 1872
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Automotive Engineering | 2024, 46(10): 1863-1872
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Vehicle Trajectory Prediction Method Based on Graph Convolutional Interaction Network
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Mengxi Wang1, Yingfeng Cai1, Hai Wang2, Zhongyu Rao1, Long Chen1, Yicheng Li1
Affiliations
  • 1. Automotive Engineering Research Institute,Jiangsu University,Zhenjiang 212013
  • 2. School of Automotive and Traffic Engineering,Jiangsu University,Zhenjiang 212013
Published: 2024-10-25 doi: 10.19562/j.chinasae.qcgc.2024.10.013
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Accurate prediction of the future trajectory of surrounding vehicles is crucial to the decision-making and motion planning of autonomous vehicle. Existing research tends to use Recurrent Neural Networks (RNN) to model the time interaction of vehicles,but its interpretability of vehicle interaction modeling is poor,ignoring the actual lane structure,and there are deficiencies in capturing the interaction between vehicles and the environment. To address this problem,in this paper,a vehicle trajectory prediction model based on graph convolutional interactive networks that considers lane topology constraints is proposed. The vehicle interaction relationship extraction module adds edge weights when constructing the spatial relationship of vehicles to consider their neighboring interaction,making the interaction more interpretable. The driving scene representation module aims to improve the accuracy of vehicle trajectory prediction by extracting lane topology from high-precision maps. The trajectory prediction module integrates the output of the above two modules and outputs the predicted future trajectory. This integration allows for more precise modeling of the interaction between road structures and vehicle driving trajectories. The experimental results show that compared with mainstream methods,this model has achieved good performance on the Argoverse dataset,improving the accuracy and rationality of vehicle trajectory planning under complex road structures.

autonomous vehicle  /  trajectory prediction  /  graph convolution network  /  interaction behavior
Mengxi Wang, Yingfeng Cai, Hai Wang, Zhongyu Rao, Long Chen, Yicheng Li. Vehicle Trajectory Prediction Method Based on Graph Convolutional Interaction Network[J]. Automotive Engineering, 2024 , 46 (10) : 1863 -1872 . DOI: 10.19562/j.chinasae.qcgc.2024.10.013
Year 2024 volume 46 Issue 10
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Article Info
doi: 10.19562/j.chinasae.qcgc.2024.10.013
  • Receive Date:2024-03-16
  • Online Date:2025-07-21
  • Published:2024-10-25
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  • Received:2024-03-16
  • Revised:2024-04-23
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    1. Automotive Engineering Research Institute,Jiangsu University,Zhenjiang 212013
    2. School of Automotive and Traffic Engineering,Jiangsu University,Zhenjiang 212013
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表12种不同金属材料的力学参数

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Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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